The research proposed in this application is designed to capitalize on and further advance two exciting new developments in statistics, hierarchical (multi-level) statistical modeling, and meta-analysis, in order to address key issues in medical effectiveness, in health services, and in health policy research. The scope of problems addressed by these methods will include: modeling and quantifying variation in utilization rates and outcomes across study units (e.g., geographic areas, service areas, hospitals, or health care providers); and formulating improved random effects and meta-analysis approaches to combine diagnostic and therapeutic procedures. Hierarchical models may be used to provide better inferences for individual units by borrowing strength of information from the ensemble. They make it possible to account properly for different levels and sources of variation in complex datasets, and they often enable analyses of questions that cannot even be formulated by standard methods. This proposal brings together a network of statistical and health policy researchers from several Harvard departments, and two national advisory groups, one composed of leading statisticians in hierarchical models, meta- analysis, probability networks and health services research, and the other composed of senior subject-matter researchers from several PORT's. This research team will accomplish the transfer of new statistical technology to health services, outcomes and policy research, it will develop new statistical methodologies and software as needed, and it will disseminate its findings to the general community of researchers in these areas via a series of prototype analyses for specific problems.